Search results by recency boosting customer support content

ABSTRACT

Disclosed methods and systems improve search results by recency boosting customer support content for a customer self-help system associated with one or more financial management systems. The customer self-help system retrieves content relevance from a variety of sources, such as media outlets, taxation agencies and news feeds for the financial management system. The customer self-help system generates content relevance weights from the content relevance data, and applies the content relevance weights to customer support content maintained by the customer self-help system. In response to receiving a search query from a user, the customer self-help system provides relevant portions of customer support content that has been recency boosted (e.g., adjusted by the content relevance weights), to increase the likelihood that the customer support content provided to the user is relevant to the user&#39;s search query.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation application of and claims priority toco-pending and commonly-owned U.S. patent application Ser. No.15/368,806 entitled “METHOD AND SYSTEM FOR IMPROVING SEARCH RESULTS BYRECENCY BOOSTING CUSTOMER SUPPORT CONTENT FOR A CUSTOMER SELF-HELPSYSTEM ASSOCIATED WITH ONE OR MORE FINANCIAL MANAGEMENT SYSTEMS” filedon Dec. 5, 2016, now U.S. Pat. No. 10,552,843, which is herebyincorporated by reference in its entirety

BACKGROUND

A customer self-help system is a system that supports or accompanies oneor more other software systems by helping users of the one or more othersoftware systems find answers to their questions, without involving livecustomer support personnel. If a customer self-help system adequatelyhelps a user find a satisfactory answer to the user's question, the useris less likely to seek addition support from live customer support(e.g., telephone support, live chat, text message, etc.). A businessbenefit of a well-functioning customer self-help system is reducedoverhead costs for a company because providing live customer support canbe expensive (e.g., sometimes costing as much as $25 per use of the livecustomer support). A user benefit of a well-functioning customerself-help system is that users can find answers to their questions morequickly than having to wait for live customer support because use oflive customer support usually involves waiting in a queue for a turn tocommunicate with a customer support representative.

Traditional customer self-help systems use content searching methodsthat employ content prioritization techniques. In some cases, theseprior art prioritization techniques inadequately match availablecustomer support content with user search queries. In particular,traditional customer self-help systems prioritize the relevance ofcustomer support content based on the creation date (e.g., file creationdate) of the customer support content. Consequently, newer customersupport content (e.g., user experience pages) are prioritized over oldercustomer support content. While this approach could be used toprioritize some customer support content (e.g., product features), suchan approach is less appropriate for customer support content that doesnot change very often (e.g., tax laws or regulations). In other words,such an approach might de-prioritize customer support content that isthe most relevant to a search query because the age of the customersupport content is older than newer customer support content, leading tosearch results that are not relevant or most relevant to a user's searchquery.

A problem with failing to provide users with information that isrelevant or most relevant to their search query is that the customerself-help system may appear useless, e.g., be irrelevant, to the users.If a user enters a search query and does not obtain search results thatare expected or that answer the user's question, then the user maycontinue to feel concerned about one or more aspects of the financialmanagement system that led the user to submitting a search query in thefirst place. A natural result is that the user will lose trust in thecustomer self-help system and possibly in any financial managementsystems associated with the customer self-help system.

Another problem with failing to provide users with information that isrelevant or most relevant to their search query is that the customerself-help system may fail to capitalize on becoming a trusted source ofinformation for users. For example, if a media outlet (e.g., AARP®,CNN®, Financial Times®, etc.) makes an announcement that is related toobtaining a particular financial advantage (e.g., based on a change totax laws), users who search a customer self-help system for moreinformation about the announcement may recognize the customer self-helpsystem as a trusted source of information. However, users who receivesearch results that appear to be oblivious to the announcement may bedisappointed and look elsewhere for guidance, both for the currentsituation and for future issues.

Another problem associated with failing to provide users withinformation that is relevant or most relevant to their search query isthat the customer self-help system may be providing users with thefaulty (e.g., out-dated) instructions. For example, if a customerself-help system provides a user with information on how to address aparticular product error, even though the product error has beenresolved, then the user is essentially being instructed to perform oneor more unnecessary steps to address a problem that is no longerrelevant.

Traditional content searching techniques include associating therelevance of searchable customer support content with the age ofcustomer support content, but the age of the customer support content isnot necessarily the dominant characteristic that determines whether thecustomer support content is relevant or highly relevant at a given pointin time. Thus, a technical problem that exists for customer self-helpsystems and search engines is a need to avoid providing less-relevant orirrelevant search results due to poor prioritization of customer supportcontent.

SUMMARY

The present disclosure includes embodiments for increasing thelikelihood of returning relevant search results to a user by recencyboosting customer support content for a customer self-help systemassociated with one or more financial management systems. Recencyboosting generally relates to increasing or decreasing a priority,ranking, or otherwise metricized relevance of customer support contentthat is managed or hosted by the customer self-help system, according toone embodiment. The customer self-help system recency boosts thecustomer support content by gathering and using content relevance datafrom a variety of sources, according to one embodiment. The customerself-help system uses the content relevance data to generate contentrelevance weights that are applied to the customer support content toincrease or decrease the relevance of the customer support content,according to one embodiment. By recency boosting the customer supportcontent, the customer self-help system increases the likelihood ofproviding relevant search results to a user, decreases the likelihood ofproviding out-dated or needless instructions to a user, and increasesthe likelihood of establishing the customer self-help system as atrusted source of information for the user, according to one embodiment.Consequently, embodiments of the disclosed customer self-help systeminclude one or more technical solutions to the technical problem ofproviding less-relevant or irrelevant search results due to poorprioritization of customer support content, according to one embodiment.

Improving search results by recency boosting customer support contentfor a customer self-help system associated with one or more financialmanagement systems is a technical solution to a long standing technicalproblem of providing irrelevant search results to users, and is not anabstract idea for at least a few reasons. First, improving searchresults by recency boosting customer support content for a customerself-help system associated with one or more financial managementsystems is not an abstract idea because it is not merely an idea itself(e.g., can be performed mentally or using pen and paper). Second,improving search results by recency boosting customer support contentfor a customer self-help system associated with one or more financialmanagement systems is not an abstract idea because it is not afundamental economic practice (e.g., is not merely creating acontractual relationship, hedging, mitigating a settlement risk, etc.).Third, improving search results by recency boosting customer supportcontent for a customer self-help system associated with one or morefinancial management systems is not an abstract idea because it is not amethod of organizing human activity (e.g., managing a game of bingo).Fourth, although mathematics may be used to generate an analytics model,the disclosed and claimed methods and systems of improving searchresults by recency boosting customer support content for a customerself-help system associated with one or more financial managementsystems are not an abstract idea because the methods and systems are notsimply a mathematical relationship/formula but are instead techniquesfor transforming customer support content into data that is more likelyto satisfy user search queries.

Further, improving search results by recency boosting customer supportcontent for a customer self-help system associated with one or morefinancial management systems is not an abstract idea because thedisclosed recency boosting allows for significant improvement to thetechnical fields of user experience, self-help systems, customerservice, customer retention, financial management systems, anddomain-specific search engines, according to one embodiment. The presentdisclosure adds significantly to the field of content searching becausethe disclosed customer self-help system: increases the likelihood that auser will receive an answer to the user's intended question; increasesthe likelihood of improving/maintaining a user's trust in the financialmanagement system; and reduces the amount of time users spend searchingfor customer support content that is relevant to the user's searchquery, according to one embodiment.

As a result, embodiments of the present disclosure allow for reduced useof processor cycles, memory, and power consumption, by reducing the timespent by users searching for answers among search results and byreducing time spent by users to repeatedly search for an answer to theirquestion in computing environments, according to one embodiment.Consequently, computing and communication systems implementing orproviding the embodiments of the present disclosure are transformed intomore operationally efficient devices and systems.

In addition to improving overall computing performance, improving searchresults by recency boosting customer support content for a customerself-help system associated with one or more financial managementsystems significantly improves the field of financial managementsystems, by reducing the amount of time it takes to progress a userthrough financial management tasks (e.g., preparing a tax return) due todelays caused by users' unanswered questions, according to oneembodiment. Furthermore, by improving search results by recency boostingcustomer support content for a customer self-help system associated withone or more financial management systems, the disclosed embodiments helpmaintain or build trust and therefore loyalty in the financialmanagement system with which the customer self-help system isassociated, which results in repeat customers, efficient delivery offinancial services, and reduced abandonment of use of the financialmanagement system, according to one embodiment.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of an architecture for improving searchresults by recency boosting customer support content for a customerself-help system associated with one or more financial managementsystems, in accordance with one embodiment.

FIGS. 2A and 2B are block diagrams of an architecture for improvingsearch results by recency boosting customer support content for acustomer self-help system associated with one or more financialmanagement systems, in accordance with one embodiment.

FIG. 3 is a diagram that illustrates one example of a technique fordetermining a relevant topic for one or more search query terms receivedfrom a user, in accordance with one embodiment.

FIGS. 4A and 4B are a flow diagram of a process for improving searchresults by recency boosting customer support content for a customerself-help system associated with one or more financial managementsystems, in accordance with one embodiment.

Common reference numerals are used throughout the FIGs. and the detaileddescription to indicate like elements. One skilled in the art willreadily recognize that the above FIGs. are examples and that otherarchitectures, modes of operation, orders of operation, andelements/functions can be provided and implemented without departingfrom the characteristics and features of the invention, as set forth inthe claims.

DETAILED DESCRIPTION

Embodiments will now be discussed with reference to the accompanyingFIGs., which depict one or more exemplary embodiments. Embodiments maybe implemented in many different forms and should not be construed aslimited to the embodiments set forth herein, shown in the FIGs., ordescribed below. Rather, these exemplary embodiments are provided toallow a complete disclosure that conveys the principles of theinvention, as set forth in the claims, to those of skill in the art.

The INTRODUCTORY SYSTEM, ARCHITECTURE, and PROCESS sections hereindescribe systems and processes suitable for improving search results byrecency boosting customer support content for a customer self-helpsystem associated with one or more financial management systems,according to various embodiments.

Introductory System

The present disclosure includes embodiments for increasing thelikelihood of returning relevant search results to a user by recencyboosting customer support content for a customer self-help systemassociated with one or more financial management systems. Recencyboosting generally relates to increasing or decreasing a priority,ranking, or otherwise metricized relevance of customer support contentthat is managed or hosted by the customer self-help system, according toone embodiment. The customer self-help system recency boosts thecustomer support content by gathering and using content relevance datafrom a variety of sources, according to one embodiment. The customerself-help system uses the content relevance data to generate contentrelevance weights that are applied to the customer support content toincrease or decrease the relevance of the customer support content,according to one embodiment. By recency boosting the customer supportcontent, the customer self-help system increases the likelihood ofproviding relevant search results to a user, decreases the likelihood ofproviding out-dated or needless instructions to a user, and increasesthe likelihood of establishing the customer self-help system as atrusted source of information for the user, according to one embodiment.Consequently, embodiments of the disclosed customer self-help systeminclude one or more technical solutions to the technical problem ofproviding less-relevant or irrelevant search results due to poorprioritization of customer support content, according to one embodiment.

As used herein, the term data management system (e.g., customerself-help system, tax return preparation system, or other softwaresystem) includes, but is not limited to the following: one or more ofcomputing system implemented, online, web-based personal and businesstax return preparation system; one or more of computing systemimplemented, online, web-based personal or business financial managementsystems, services, packages, programs, modules, or applications; one ormore of computing system implemented, online, and web-based personal orbusiness management systems, services, packages, programs, modules, orapplications; one or more of computing system implemented, online, andweb-based personal or business accounting or invoicing systems,services, packages, programs, modules, or applications; and variousother personal or business electronic data management systems, services,packages, programs, modules, or applications, whether known at the timeof filling or as developed after the time of filing.

Specific examples of data management systems include financialmanagement systems. Examples of financial management systems include,but are not limited to the following: TurboTax® available from Intuit,Inc. of Mountain View, Calif.; TurboTax® Online™ available from Intuit,Inc. of Mountain View, Calif.; QuickBooks®, available from Intuit, Inc.of Mountain View, Calif.; QuickBooks® Online™, available from Intuit,Inc. of Mountain View, Calif.; Mint®, available from Intuit, Inc. ofMountain View, Calif.; Mint® Online, available from Intuit, Inc. ofMountain View, Calif.; or various other systems discussed herein, orknown to those of skill in the art at the time of filing, or asdeveloped after the time of filing.

As used herein, the terms “computing system,” “computing device,” and“computing entity,” include, but are not limited to, the following: aserver computing system; a workstation; a desktop computing system; amobile computing system, including, but not limited to, one or more ofsmart phones, portable devices, and devices worn or carried by a user; adatabase system or storage cluster; a virtual asset; a switching system;a router; any hardware system; any communications system; any form ofproxy system; a gateway system; a firewall system; a load balancingsystem; or any device, subsystem, or mechanism that includes componentsthat can execute all, or part, of any one of the processes or operationsas described herein.

In addition, as used herein, the terms “computing system”, “computingentity”, and “computing environment” can denote, but are not limited tothe following: systems made up of multiple virtual assets, servercomputing systems, workstations, desktop computing systems, mobilecomputing systems, database systems or storage clusters, switchingsystems, routers, hardware systems, communications systems, proxysystems, gateway systems, firewall systems, load balancing systems, orany devices that can be used to perform the processes or operations asdescribed herein.

Herein, the term “production environment” includes the variouscomponents, or assets, used to deploy, implement, access, and use, agiven system as that system is intended to be used. In variousembodiments, production environments include multiple computing systemsor assets that are combined, communicatively coupled, virtually orphysically connected, or associated with one another, to provide theproduction environment implementing the application.

As specific illustrative examples, the assets making up a givenproduction environment can include, but are not limited to, thefollowing: one or more computing environments used to implement at leastpart of a system in the production environment such as a data center, acloud computing environment, a dedicated hosting environment, or one ormore other computing environments in which one or more assets used bythe application in the production environment are implemented; one ormore computing systems or computing entities used to implement at leastpart of a system in the production environment; one or more virtualassets used to implement at least part of a system in the productionenvironment; one or more supervisory or control systems, such ashypervisors, or other monitoring and management systems used to monitorand control assets or components of the production environment; one ormore communications channels for sending and receiving data used toimplement at least part of a system in the production environment; oneor more access control systems for limiting access to various componentsof the production environment, such as firewalls and gateways; one ormore traffic or routing systems used to direct, control, or buffer datatraffic to components of the production environment, such as routers andswitches; one or more communications endpoint proxy systems used tobuffer, process, or direct data traffic, such as load balancers orbuffers; one or more secure communication protocols or endpoints used toencrypt/decrypt data, such as Secure Sockets Layer (SSL) protocols, usedto implement at least part of a system in the production environment;one or more databases used to store data in the production environment;one or more internal or external services used to implement at leastpart of a system in the production environment; one or more backendsystems, such as backend servers or other hardware used to process dataand implement at least part of a system in the production environment;one or more modules/functions used to implement at least part of asystem in the production environment; or any other assets/componentsmaking up an actual production environment in which at least part of asystem is deployed, implemented, accessed, and run, e.g., operated, asdiscussed herein, or as known in the art at the time of filing, or asdeveloped after the time of filing.

As used herein, the term “computing environment” includes, but is notlimited to, a logical or physical grouping of connected or networkedcomputing systems or virtual assets using the same infrastructure andsystems such as, but not limited to, hardware systems, systems, andnetworking/communications systems. Typically, computing environments areeither known, “trusted” environments or unknown, “untrusted”environments. Typically, trusted computing environments are those wherethe assets, infrastructure, communication and networking systems, andsecurity systems associated with the computing systems or virtual assetsmaking up the trusted computing environment, are either under thecontrol of, or known to, a party.

In various embodiments, each computing environment includes allocatedassets and virtual assets associated with, and controlled or used tocreate, deploy, or operate at least part of the system.

In various embodiments, one or more cloud computing environments areused to create, deploy, or operate at least part of the system that canbe any form of cloud computing environment, such as, but not limited to,a public cloud; a private cloud; a virtual private network (VPN); asubnet; a Virtual Private Cloud (VPC); a sub-net or anysecurity/communications grouping; or any other cloud-basedinfrastructure, sub-structure, or architecture, as discussed herein, asknown in the art at the time of filing, or as developed after the timeof filing.

In many cases, a given system or service may utilize, and interfacewith, multiple cloud computing environments, such as multiple VPCs, inthe course of being created, deployed, or operated.

As used herein, the term “virtual asset” includes any virtualized entityor resource, or virtualized part of an actual, or “bare metal” entity.In various embodiments, the virtual assets can be, but are not limitedto, the following: virtual machines, virtual servers, and instancesimplemented in a cloud computing environment; databases associated witha cloud computing environment, or implemented in a cloud computingenvironment; services associated with, or delivered through, a cloudcomputing environment; communications systems used with, part of, orprovided through a cloud computing environment; or any other virtualizedassets or sub-systems of “bare metal” physical devices such as mobiledevices, remote sensors, laptops, desktops, point-of-sale devices, etc.,located within a data center, within a cloud computing environment, orany other physical or logical location, as discussed herein, or asknown/available in the art at the time of filing, or as developed/madeavailable after the time of filing.

In various embodiments, any, or all, of the assets making up a givenproduction environment discussed herein, or as known in the art at thetime of filing, or as developed after the time of filing can beimplemented as one or more virtual assets within one or more cloud ortraditional computing environments.

In one embodiment, two or more assets, such as computing systems orvirtual assets, or two or more computing environments are connected byone or more communications channels including but not limited to, SecureSockets Layer (SSL) communications channels and various other securecommunications channels, or distributed computing system networks, suchas, but not limited to the following: a public cloud; a private cloud; avirtual private network (VPN); a subnet; any general network,communications network, or general network/communications networksystem; a combination of different network types; a public network; aprivate network; a satellite network; a cable network; or any othernetwork capable of allowing communication between two or more assets,computing systems, or virtual assets, as discussed herein, or availableor known at the time of filing, or as developed after the time offiling.

As used herein, the term “network” includes, but is not limited to, anynetwork or network system such as, but not limited to, the following: apeer-to-peer network; a hybrid peer-to-peer network; a Local AreaNetwork (LAN); a Wide Area Network (WAN); a public network, such as theInternet; a private network; a cellular network; any general network,communications network, or general network/communications networksystem; a wireless network; a wired network; a wireless and wiredcombination network; a satellite network; a cable network; anycombination of different network types; or any other system capable ofallowing communication between two or more assets, virtual assets, orcomputing systems, whether available or known at the time of filing oras later developed.

As used herein, the term “user experience display” includes not onlydata entry and question submission user interfaces, but also other userexperience features and elements provided or displayed to the user suchas, but not limited to, the following: data entry fields, questionquality indicators, images, backgrounds, avatars, highlightingmechanisms, icons, buttons, controls, menus and any other features thatindividually, or in combination, create a user experience, as discussedherein, or as known in the art at the time of filing, or as developedafter the time of filing.

As used herein, the terms “user experience page” and “user experiencescreen” are interchangeable in meaning and represent a changeablerendering or view of content that is provided to a user in the userexperience display, according to one embodiment.

As used herein, the term “user experience” includes, but is not limitedto, one or more of a search query creation process, an incrementalsearch results receipt process, a user session, interview process,interview process questioning, or interview process questioningsequence, or other user experience features provided or displayed to theuser such as, but not limited to, interfaces, images, assistanceresources, backgrounds, avatars, highlighting mechanisms, icons, and anyother features that individually, or in combination, create a userexperience, as discussed herein, or as known in the art at the time offiling, or as developed after the time of filing.

Herein, the term “party,” “user,” “user consumer,” and “customer” areused interchangeably to denote any party or entity that interfaces with,or to whom information is provided by, the disclosed methods and systemsdescribed herein, or a legal guardian of person or entity thatinterfaces with, or to whom information is provided by, the disclosedmethods and systems described herein, or an authorized agent of anyparty or person or entity that interfaces with, or to whom informationis provided by, the disclosed methods and systems described herein. Forinstance, in various embodiments, a user can be, but is not limited to,a person, a commercial entity, an application, a service, or a computingsystem.

As used herein, the term “analytics model” denotes one or moreindividual or combined algorithms or sets of ordered relationships thatdescribe, determine, or predict characteristics of or the performance ofa datum, a data set, multiple data sets, a computing system, or multiplecomputing systems. Analytics models or analytical models representcollections of measured or calculated behaviors of attributes, elements,or characteristics of data or computing systems. Analytics modelsinclude probabilistic topic models and predictive models (e.g., queryclassifiers), which identify the likelihood of one attribute orcharacteristics based on one or more other attributes orcharacteristics.

As used herein, the terms “interview” and “interview process” include,but are not limited to, an electronic, software-based, or automateddelivery of multiple questions to a user and an electronic,software-based, or automated receipt of responses from the user to thequestions, to progress a user through one or more groups or topics ofquestions, according to various embodiments.

As used herein the term “self-help system” is interchangeable with“self-service system” and “self-support system”. A self-help system(e.g., a customer self-help system) is a system that enables customersand other users to help themselves find answers to questions, findspecific content within a financial management system, navigate withinthe financial management system, or perform one or more actions (e.g.,adjust the user tax data within a particular form), according to oneembodiment. In contrast, the term “live customer support” denotes aninteraction between a user of a financial management system and acustomer support representative who uses a telephone call, instantmessaging, a video conference, text messaging, or other mode oftelecommunications to resolve questions or concerns of a user of thefinancial management system.

As used herein, the term “incremental search results” includes searchresults that are generated for search query terms based on the variousinformation a financial management system has for a user, as a userenters the search query terms (e.g., in real-time) as part of a searchquery to a customer self-help system, according to one embodiment.Incremental search results include progressively searched for andfiltered through text. As the user types, one or more possible matchesfor the text are found and immediately presented to the user, accordingto one embodiment. The incremental search results are specific to thedomain of a financial management system and may include automatedsuggestions for additional search query terms that are specific tosearching the financial management system, according to one embodiment.Because the incremental search results are specific to the domain of thefinancial management system, as used herein, “incremental searchresults” is used interchangeably with “domain-specific incrementalsearch results”. The incremental search results are search results for apartial or complete search query that is based on the received searchquery terms and based on user tax data (e.g., user profile data),according to one embodiment. The incremental search results are searchresults for one or more potential search queries that a user couldformulate from search query terms that have been received by thecustomer self-help system, according to one embodiment.

As used herein the term “recency boost” denotes increasing or decreasinga priority, ranking, or otherwise metricized relevance of customersupport content that is managed or hosted by a customer self-helpsystem, according to one embodiment. Recency boosting denotes the use ofinformation other than a user's search criteria or user's information toprioritize, rank, or otherwise metricize the relevance of customersupport content, according to one embodiment. Recency boostingincreases, decreases, or maintains a priority, ranking, or otherwisemetricized relevance of customer support content based on sources ofcontent relevance data such as one or more of: media outlets, taxationagencies, a financial management system, a financial management systemservice provider, a customer self-help system, and a customer self-helpsystem service provider, according to one embodiment.

Architecture

FIG. 1 is an example block diagram of a production environment 100 forimproving search results by recency boosting customer support contentfor a customer self-help system associated with one or more financialmanagement systems, according to one embodiment. The productionenvironment 100 includes a customer self-help system 110 that recencyboosts customer support content based on content relevance data 130 thatis received from one or more of a variety of sources of contentrelevance data (discussed below), according to one embodiment. Thecustomer self-help system 110 applies one or more recency boostingtechniques to customer support content that is managed by the customerself-help system 110, to improve the likelihood that a user will receiverelevant customer support content in response to submitting a searchquery to the customer self-help system 110, according to one embodiment.The customer self-help system 110 retrieves the content relevance data130 from one or more of a variety of sources, in order to determine andapply relevance weighting to customer support content that is managed bythe customer self-help system 110, according to one embodiment. Byapplying relevance weighting to the customer support content, thecustomer self-help system 110 increases the likelihood that the searchresults that are provided in response to a received search query, takeinto account news or information updates related to the search query.

The customer self-help system 110 is communicatively coupled to receivethe content relevance data 130 through a network 101, according to oneembodiment. The network 101 represents the Internet, according to oneembodiment. The customer self-help system 110 is communicatively coupledto one or more of a variety of sources of the content relevance data 130through one or more of communications channels 102, 103, 104, and 105,according to one embodiment.

The content relevance data 130 is provided by one or more of a varietyof sources, according to one embodiment. The sources of the contentrelevance data 130 include one or more of a financial management system132, a taxation agency 134, and media outlets 136, according to oneembodiment. The financial management system 132 includes one or more ofa tax return preparation system, a business financial management system,and a personal financial management system (not shown), according to oneembodiment. The financial management system 132 includes, but is notlimited to, one or more of: TurboTax® available from Intuit, Inc. ofMountain View, Calif.; TurboTax® Online™ available from Intuit, Inc. ofMountain View, Calif.; QuickBooks®, available from Intuit, Inc. ofMountain View, Calif.; QuickBooks® Online™, available from Intuit, Inc.of Mountain View, Calif.; Mint®, available from Intuit, Inc. of MountainView, Calif.; Mint® Online, available from Intuit, Inc. of MountainView, Calif.; or various other systems discussed herein, or known tothose of skill in the art at the time of filing, or as developed afterthe time of filing, according to various embodiments.

The financial management system 132 provides different types of contentrelevance data 130, from which the customer self-help system 110determines content relevance weights that are used to recency boostcustomer support content, according to one embodiment. The financialmanagement system 132 provides product news data 138 and self-helpsearch trends data 140, according to one embodiment. The product newsdata 138 includes announcements from the service provider for thefinancial management system 132, according to one embodiment. Theannouncements from the service provider of the financial managementsystem 132 include, but are not limited to, news or updates aboutproduct errors that have been recently detected, news or updates aboutproduct errors that have recently been fixed, news or updates aboutproduct features that are planned for future releases of the financialmanagement system 132, news or updates about product features that havebeen released, and news or updates about product features that have beenretired or otherwise removed from the financial management system 132,according to one embodiment.

The self-help search trends data 140 includes various trends in searchqueries that are submitted to the financial management system 132 or tothe customer self-help system 110, according to one embodiment. Thetrends in search queries include prevalent search queries, dominantsearch queries, or trending search queries that are submitted to thefinancial management system 132 or to the customer self-help system 110,according to one embodiment. The self-help search trends data 140represents search queries that are trending within one or more windowsof time (e.g., within tax season, within a fourth quarter of the fiscalyear, within the last week, etc.), according to one embodiment. Theself-help search trends data 140 represents search queries that aretrending based on a comparison to all search queries received by thefinancial management system 132 or by the customer self-help system 110,according to one embodiment. Self-help search trends data 140 thatrepresent trends that are based on a comparison are determined based ona percentage or quantity of search queries that are above an average orthat deviate from a typical or expected trend in search querysubmissions, according to one embodiment.

The financial management system 132 or the service provider for thefinancial management system 132 provides the product news data 138 inone or more electronic media formats such as, but not limited to, RichSite Summary (“RSS”) feeds, email updates, website updates, andelectronic notification updates, according to one embodiment.

The financial management system 132 or the customer self-help system 110determines, maintains, and provides the self-help search trends data140, based on search queries submitted to the financial managementsystem 132 and to the customer self-help system 110, according to oneembodiment.

The taxation agency 134 is another source of the content relevance data130, according to one embodiment. Examples of the taxation agency 134include, but are not limited to, one or more of the Internal RevenueService, state taxation agencies (e.g., the California Tax ServiceCenter, the New York State Department of Taxation and Finance, etc.),and other federal or state government or private taxation agencies orservices, according to various embodiments. The taxation agency 134provides tax law updates data 142 that represent updates to federal taxlaw, updates to state tax law, or a combination of updates to federaltax law and state tax law, according to one embodiment. Similar to theproduct news data 138, the taxation agency 134 makes the tax law updatesdata 142 available in one or more electronic media formats such as, butnot limited to, RSS feeds, email updates, website updates, andelectronic notification updates, according to one embodiment. The taxlaw updates data 142 may include content that will stay current for along time (e.g., tax law) as well as content that can quickly becomeoutdated (e.g., tax filing season updates), according to one embodiment.A specific illustrative example of an e-news subscription that isavailable for acquiring tax law updates from the Internal RevenueService is IRS Newswire, which can include two to three news releases ona weekly basis about topics such as tax administration, tax law changes,and tax filing season updates.

Media outlets 136 are another source of the content relevance data 130,according to one embodiment. Examples of media outlets 136 include, butare not limited to, one or more of electronic magazines, electronicnewspapers, and websites, according to one embodiment. The media outlets136 are financial information media outlets (e.g., the financial Times,CNN money, etc.), according to one embodiment. A specific illustrativeexample of financial news data 144 is an electronically-availablearticle that discusses tips on reducing a federal tax liability bytaking advantage of energy tax credits made available by the federalgovernment for one or more predetermined periods of time.

The customer self-help system 110 receives or acquires the contentrelevance data 130 and uses the content relevance data 130 to adjust(i.e., recency boost) the relevance of customer support content based onrecent news, changes in laws, and product announcements, according toone embodiment. By using the content relevance data 130 to adjust therelevance of customer support content, the customer self-help system 110provides customer support content that is prioritized orrelevance-weighted at least partially based on the product news data138, the self-help search trends data 140, the tax law updates data 142,and the financial news data 144, according to one embodiment.

The customer self-help system 110 uses a content relevance engine 112 toacquire the content relevance data 130, according to one embodiment. Thecontent relevance engine 112 acquires the content relevance data 130using one or more of a variety of Internet information acquisitiontechniques, according to one embodiment. The content relevance engine112 uses a RSS feed reading scripts, libraries, or tools that areavailable in or developable with Python, Perl, PHP, JavaScript, or oneor more other programming languages that can be used to interface withsources of the content relevance data 130 over the network 101,according to one embodiment. The content relevance engine 112 subscribesto one or more email or electronic delivery alert systems and uses oneor more scripts, libraries, or tools in Python, Perl, PHP, JavaScript,or one or more other programming languages to parse the email orelectronic delivery alerts from the sources of the content relevancedata 130, according to one embodiment.

The customer self-help system 110 uses the content relevance engine 112to determine content relevance topics 114 and content relevance weights116, which are used to recency boost the customer support content thatis provided to users in response to search query submissions, accordingto one embodiment. The content relevance engine 112 generates thecontent relevance topics 114 by applying the content relevance data 130to a content relevance analytics model 113, according to one embodiment.The content relevance analytics model 113 includes, but is not limitedto, one or more of a probabilistic topic model and a predictive model,according to one embodiment. A probabilistic topic model receives textand identifies one or more topics that are prevalent throughout thereceived text, according to one embodiment. The probabilistic topicmodel is implemented using one or more of a Latent Dirichlet Allocation(“LDA”) algorithm, Latent Semantic Indexing (“LSI”), query clustering,query de-duplication, and one or more other techniques currently knownor later developed for generating probabilistic topic models, accordingto various embodiments. A predictive model identifies the likelihoodthat one or more of the content relevance topics 114 are relevant to thecontent relevance data 130 by training the predictive model onhistorical content relevance data from which the content relevancetopics 114 have already been identified using other techniques,according to one embodiment. The predictive model used by the contentrelevance engine 112 is trained using predictive model trainingoperations that include, but are not limited to, one or more ofregression, logistic regression, decision trees, artificial neuralnetworks, support vector machines, linear regression, nearest neighbormethods, distance based methods, naive Bayes, linear discriminantanalysis, k-nearest neighbor algorithm, another query classifier, andany other presently known or later developed predictive model trainingoperations, according to one embodiment.

The content relevance topics 114 represent one or more topics that areextracted by the content relevance engine 112 from the content relevancedata 130, according to one embodiment. The content relevance topics 114include information or subject matter that is relevant to someone usingthe financial management system 132, according to one embodiment.Specific illustrative examples of the content relevance topics 114include invoicing, tracking expenses, receipts, accounts payable,accounts receivable, expense tracking, product care, taxes, income,deductions, retirement accounts, budgets, personal spending, accountmanagement, user authentication, scams, and security threats, accordingto various embodiments.

The content relevance engine 112 determines the content relevanceweights 116, according to one embodiment. The content relevance engine112 determines the content relevance weights 116 at least partiallybased on the content relevance topics 114 that are extracted or derivedfrom the content relevance data 130, according to one embodiment. Thecontent relevance weights 116 are proportional to the quantities ofcontent relevance topics 114 that are derived from the content relevancedata 130, according to one embodiment. The content relevance weights 116are determined based on the prevalence of the content relevance topics114 in the content relevance data 130, according to one embodiment. Asan example, if one of the content relevance topics 114 (e.g. cybersecurity) appeared 100 times in the content relevance data 130 for aparticular period of time, then the content relevance weights 116 forthe one of the content relevance topics 114 will be relatively high. Asanother example, if one of the content relevance topics 114 (e.g. energycredits) appears only once in the content relevance data 130 for aparticular period of time, then the content relevance engine 112 weightsthat one of the content relevance topics 114 relatively low, accordingto one embodiment.

The content relevance weights 116 can span one of a number of differentranges of values. In one embodiment, the range of values for the contentrelevance weights 116 is 0-1, with values that are closer to 1 beinghighly relevant and values being closer to 0 being less relevant,according to one embodiment. In one embodiment, the content relevanceweights 116 span from 0-2, and a topic or customer support contentreceives a weight of less than 1 to decrease its relevance, a weight ofgreater than 1 to increase its relevance, and a value of 1 to maintainits relevance, according to one embodiment. While specific illustrativeexamples of ranges include 0-1 and 0-2, any range of numbers arepossible for use in weighting the relevance of customer support contentbased on content relevance data 130, according to one embodiment.

The customer self-help system 110 uses the content relevance weights 116to update and maintain a content relevance table 118, according to oneembodiment. The content relevance table 118 and content relevanceweights 116 are used to boost or buck (i.e., increase or decrease) therelevance of the customer support content that is managed by thecustomer self-help system 110, according to one embodiment. The contentrelevance table 118 includes a topic column 120, a content identifiercolumn 122, and a content relevance weights column 124, as examplecolumns of the content relevance table 118, according to one embodiment.The content relevance table 118 can include more columns thanillustrated, or fewer columns than illustrated, according to variousembodiments. The topic column 120 represents a topical categorization ofthe customer support content that is available to provide to a user inresponse to a search query, according to one embodiment. The contentidentifier column 122 represents identification (e.g., numerical ordescriptive) of various types of customer support content that can beprovided to the user in response to receiving a search query from theuser, according to one embodiment. Illustrative examples of contentinclude, but are not limited to, a 401(k) calculator, an energy creditstutorial, a tax error announcement, browser settings instructions, apotential search query “how to create an invoice”, and a potentialsearch query “how to estimate quarterly taxes”, according to oneembodiment. The content relevance weights column 124 is populated withthe content relevance weights 116 that are determined by the contentrelevance engine 112, according to one embodiment. In the illustratedexample of the content relevance table 118, the values of the contentrelevance weights 116 that are included in the content relevance weightscolumn 124 span the value of 0-2, with values closer to 0 having a lowerrelevance to users and with values closer to 2 having a higher relevanceto users, according to one embodiment.

Row 126 of the content relevance table 118 represents one specificillustrative example of a topic, content identifier, and contentrelevance weight that can be included in the content relevance table118, based on the content relevance data 130, according to oneembodiment. If, for example, the product news data 138 includes ataxpayer announcement that the financial management system 132erroneously calculated state tax liabilities and state tax refunds for aparticular state, the content relevance engine 112 may assign a value of1.2 to the content relevance weights 116 for the topic of producterrors, and more specifically for content related to a taxpayerannouncement, according to one embodiment. By assigning a value of 1.2to the content relevance weights 116 that are associated with thetaxpayer announcement, the customer self-help system 110 uses thecontent relevance table 118 to increase or recency boost the taxpayerannouncement, so that the search engine used by the customer self-helpsystem 110 is more likely to provide the taxpayer announcement to theuser, in response to receiving a search query, according to oneembodiment.

Row 127 of the content relevance table 118 represents one specificillustrative example of a topic, content identifier, and contentrelevance weight that can be included in the content relevance table118, based on the content relevance data 130, according to oneembodiment. If, for example, the financial management system 132previously included a product error that was associated with needing toadjust web browser settings to properly view content for the financialmanagement system 132, and the error no longer exists because it hasbeen resolved, then the content of browser settings instructionsrepresents a potential search result that will annoy or confuse usersbecause it provides instructions to adjust browser settings that nolonger need to be adjusted, according to one embodiment. As a result,the customer self-help system 110 assigned the value of 0.2 as thecontent relevance weights 116 for the topic of product error that isassociated with the content identifier for browser settingsinstructions, to decrease the likelihood of the user receiving thebrowser settings instructions, and needlessly adjusting their webbrowser settings, according to one embodiment.

The customer self-help system 110 provides recency boosted customersupport content 128 in response to receiving a search query 129 from theuser, according to one embodiment. The customer self-help system 110uses the search query 129 to identify relevant portions of customersupport content, and the customer self-help system 110 uses the contentrelevance table 118 to further distinguish relevant customer supportcontent from less relevant customer support content, according to oneembodiment. The customer self-help system 110 then provides the recencyboosted or distinguished customer support content 128 to a user of thecustomer self-help system 110, according to one embodiment.

FIGS. 2A and 2B are an example block diagram of a production environment200 for improving search results by recency boosting customer supportcontent for a customer self-help system associated with one or morefinancial management systems, according to one embodiment. Theproduction environment 200 includes a number of components in commonwith the production environment 100 (shown in FIG. 1), and the commoncomponents are identified using the same reference number in bothproduction environments 100 and 200. The production environment 200illustrates techniques for identifying customer support content that isrelevant to a search query, and is used to describe the use of contentrelevance weights in prioritizing customer support content, according toone embodiment. The production environment 200 includes the customerself-help system 110, the financial management system 132, and usercomputing systems 290 that are communicatively coupled through a network201, according to one embodiment. All or part of the modules,functionality, or components of the customer self-help system 110 arehosted in a customer self-help system computing environment 205,according to one embodiment. All or part of the modules, functionality,or components of the financial management system 132 are hosted in afinancial management system computing environment 206, according to oneembodiment. All or part of the modules, functionality, or components ofthe customer self-help system 110 and the financial management system132 are hosted in the customer self-help system computing environment205 or in the financial management system computing environment 206,according to one embodiment. All or part of the modules, functionality,or components of the customer self-help system 110 and the financialmanagement system 132 are hosted in one or more of the customerself-help system computing environment 205, the financial managementsystem computing environment 206, and one or more additional computingenvironments (not shown), according to one embodiment. The customerself-help system 110, the financial management system 132, and the usercomputing systems 290 are communicatively coupled to one another throughthe network 201 through one or more communications channels 202, 203,and 204, according to one embodiment. The network 201 is the Internet,according to one embodiment.

The customer self-help system 110 includes a customer self-help systemengine 212, a search engine 214, potential search queries 216, userprofile data 217, and user financial data 218 to identify and delivercustomer support content that is relevant to a search query receivedfrom a user, according to one embodiment. The customer self-help system110 also includes the content relevance engine 112, the contentrelevance table 118, and the content relevance data 130 that are used bythe customer self-help system 110 to improve search results by recencyboosting customer support content based on the content relevance data130, according to one embodiment. The customer self-help system engine212 receives one or more complete or partial search query terms fromusers of the customer self-help system 110, and provides recency boostedcustomer support content to the users in response to receipt of thesearch query terms, according to one embodiment. The customer self-helpsystem 110 uses the search engine 214 to search for customer supportcontent that is relevant to the search query terms, at least partiallybased on the search query terms, the user profile data 217, and the userfinancial data 218, according to one embodiment. The search engine 214,the customer self-help system engine 212, or the customer self-helpsystem 110 defines the recency boosted customer support content that isdelivered to users in response to search query terms or search queriesreceived from the users, according to one embodiment.

The customer self-help system engine 212 or the customer self-helpsystem 110 provides a user experience display 220 to receive a searchquery 129 from a user and to provide recency boosted customer supportcontent 128 to the user, according to one embodiment. The userexperience display 220 includes, but is not limited to, one or more oftext boxes, menus, buttons, avatars, audio/video content, and other userexperience elements to receive the search query 129 and to provide therecency boosted customer support content 128, according to oneembodiment. The user experience display 220 includes a search text box(not shown) that the user uses to enter the search query 129 with one ormore search query terms 226, according to one embodiment. The userexperience display 220 includes a results text box (not shown) that isvisible to the user to allow the user to receive the recency boostedcustomer support content 128, according to one embodiment. The resultstext box is displayed adjacent to the search text box, according to oneembodiment.

The customer self-help system 110 uses the search engine 214 to searchthe customer support content 240 for content to include in the recencyboosted customer support content 128, based on one or more of the searchquery terms 226, the user financial data 218, and the user profile data217, according to one embodiment.

In one embodiment, the customer self-help system 110 includes one ormore potential search queries 216 within the recency boosted customersupport content 128. The potential search queries 216 represent searchqueries that have been formulated or submitted to the customer self-helpsystem 110 or to the financial management system 132 by a current useror by one or more prior users of the customer self-help system 110 or ofthe financial management system 132, according to one embodiment. Thepotential search queries 216 are stored in one or more tables,databases, or other data structures, that store records of previouslyreceived or defined search queries, according to one embodiment. In oneembodiment, each of the potential search queries 216 is considered atopic, and each of the potential search queries 216 is associated withone or more topic terms, one or more topic term probabilities, userprofile characteristics, and user financial data characteristics. Thesearch engine 214 identifies potential search queries 216 that includethe same topic or topics as one or more received search query terms 226,and provides (to the user) customer support content 240 that isresponsive to the more relevant one of the potential search queries 216,as recency boosted customer support content 128, according to oneembodiment.

The search engine 214 or the customer self-help system 110 uses the userfinancial data 218 to identify which portions of the customer supportcontent 240 to include in the recency boosted customer support content128, according to one embodiment. The user financial data 218 includesone or more of user tax data, user business data, and user personalfinancial data, according to one embodiment. As a specific illustrativeexample, the search engine 214 or the customer self-help system 110provides a user's state tax refund amount (e.g., of $331.11) in anincremental search result that is included in the recency boostedcustomer support content 128, in response to receiving a search query129 of “tax refund”. The user financial data 218 is stored in one ormore tables, databases, or other data structures, according to oneembodiment. The user financial data 218 includes existing user financialdata received in a current year or in prior years from a present user orfrom other users, according to one embodiment. The user financial data218 includes new user financial data, which represents user financialdata acquired from or about a present user (e.g., the submitter of thesearch query terms) during a current user session with the customerself-help system 110 or with the financial management system 132,according to one embodiment. The user financial data 218 includes anyuser information or tax information that has been used, that can beused, or that may be used at a future date to prepare a user's taxreturn, to manage a user's business, or to manage a user's personalfinances, according to one embodiment. Examples of the user financialdata 218 include, but are not limited to, one or more of a user's name,a date of birth, an address, a zip code, a home ownership status, amarital status, an annual income, a job title, an employer's address,spousal information, children's information, asset information, medicalhistory, occupation, information regarding dependents, salary and wages,interest income, dividend income, business income, farm income, capitalgain income, pension income, individual retirement account (“IRA”)distributions, unemployment compensation, education expenses, healthsavings account deductions, moving expenses, IRA deductions, studentloan interest deductions, tuition and fees, medical and dental expenses,state and local taxes, real estate taxes, personal property tax,mortgage interest, charitable contributions, casualty and theft losses,unreimbursed employee expenses, alternative minimum tax, foreign taxcredit, education tax credits, retirement savings contribution, childtax credits, business income, accounts receivable, accounts payable,invoice information, inventory quantities, inventory costs, operatingexpenses, business travel records, business travel expenses, customercontact information, credit card balances, quarterly tax estimations,spending category budgets, outstanding loan balances, personal spendingtrends, categories of business expenses, categories of personalexpenses, employee information, employee expenses, insurance costs, andresidential energy credits.

The customer self-help system 110 receives the user financial data 218from the financial management system 132, according to one embodiment.The customer self-help system 110 is optionally integrated into thefinancial management system 132 and uses data retrieval commands orcalls to retrieve at least part of the user financial data 218 for usein determining the recency boosted customer support content 128,according to one embodiment. The content of the user financial data 218or the values of the user financial data 218 are user financial datacharacteristics, according to one embodiment.

The search engine 214 uses a search algorithm 228 that uses acombination of one or more the user profile data 217 and the userfinancial data 218, to provide context to the search query terms inorder to select which portions of the customer support content 240 arerelevant to the search query 129, according to one embodiment. The userprofile data 217 includes records of actions (e.g., user experiencepages visited) in both current and prior search sessions, which thecustomer self-help system 110 uses to identify topics or customersupport content 240 that is relevant to the search query 129, accordingto one embodiment. The user profile data 217 includes, but is notlimited to, the user's navigation or browsing history within thefinancial management system 132, the user's search history within thefinancial management system 132, the user's navigation (e.g., interviewscreen visitation history), browsing, or search history within thecustomer self-help system 110, clickstream data for the user, estimatededucation level of the user, user-experience with the financialmanagement system 132 (e.g., a repeat versus first-time user), InternetProtocol (“IP”) address characteristics (e.g., library, corporation,residential, foreign IP address, etc.), and hardware or softwareconfigurations of the user's computing device (e.g., Mac, PC, mobiledevice, operating system, etc.), according to various embodiments.

The search engine 214 or the customer self-help system 110 uses thesearch algorithm 228 to define the recency boosted customer supportcontent 128, according to one embodiment. The search engine 214 receivesthe search query terms 226 from the customer self-help system engine 212and applies the search query terms 226 to the search algorithm 228 togenerate/define the recency boosted customer support content 128,according to one embodiment. The search engine 214 uses the searchalgorithm 228 to search the potential search queries 216 to determinewhich one of the potential search queries 216 is likely to be relevantto the user, in the context of the search query terms 226, the userfinancial data 218, and the user profile data 217, according to oneembodiment. Relevant ones of the potential search queries 216 andanswers to the relevant ones of the potential search queries are used topopulate the recency boosted customer support content 128, according toone embodiment. In one embodiment, the search algorithm 228 employs oneor more of a probabilistic topics model, query clustering, queryde-duplication, Latent Dirichlet allocation algorithm, one or more otherpredictive models, and one or more other database or content searchingalgorithms for matching one or more of the search query terms 226, theuser profile data 217, and the user financial data 218 to relevant onesof the potential search queries 216, according to one embodiment. Thesearch engine 214 uses the search algorithm 228 to match one or more ofthe search query 129, with context from the user profile data 217 andthe user financial data 218, to relevant portions of the customersupport content 240, according to one embodiment.

The search algorithm 228 includes or uses a probabilistic topic model238 to identify topics for the search query terms 226, for the searchquery 129, and for the potential search queries 216, according to oneembodiment. The probabilistic topic model 238 receives the potentialsearch queries 216 or the search query terms 226 and identifies thetopics of the potential search queries 216 or the topics of the searchquery terms 226, according to one embodiment. The probabilistic topicmodel 238 utilizes one or more of the Latent Dirichlet allocationalgorithm, Latent Semantic Indexing (“LSI”), query clustering, queryde-duplication, and one or more other techniques currently known orlater developed for generating probabilistic topic models, to identifythe topics of the potential search queries 216 or the topics of thesearch query terms 226, according to one embodiment. The search engine214 uses the topics of the potential search queries 216 or the topics ofthe search query terms 226 to match relevant ones of the potentialsearch queries 216 with the search query terms 226, according to oneembodiment. The search engine 214 selects one or more relevant ones ofthe potential search queries 216 and answers to the relevant ones of thepotential search queries as potential content for the recency boostedcustomer support content 128, according to one embodiment.

The customer self-help system 110 uses the probabilistic topic model 238(e.g., utilizing the Latent Dirichlet allocation algorithm) to populatethe topics database 258 to determine the relevance between the searchquery terms 226 and the customer support content 240, according to oneembodiment. The customer self-help system 110 maintains a topicalcorrelation or mapping between the customer support content 240 andparticular topics 260, which enables topical search and identificationof user experience pages and other content within the customer supportcontent 240, according to one embodiment. The topics database 258includes topics 260, topic terms 262, the topic term probabilities 264,user profile characteristics 265, user profile characteristicsprobabilities 266, user financial data characteristics 267, and userfinancial data characteristics probabilities 268, according to oneembodiment.

The topics 260 represent themes, subjects, or categories of informationthat are included in one or more of the potential search queries 216,the user financial data 218, the user profile data 217, and the customersupport content 240, according to one embodiment. The topics 260 areidentified by applying one or more of the potential search queries 216,the user financial data 218, the user profile data 217, and the customersupport content 240 to the probabilistic topic model 238, according toone embodiment. As a result, the topics 260 represent themes, subjects,or categories of information stored in: the potential search queries216, the user financial data 218, the user profile data 217, and thecustomer support content 240, according to one embodiment.

Specific illustrative examples of the topics 260 include, but are notlimited to, one or more of product errors, security threats, scams,budgeting, receipts, invoices, tax estimates, authentication, accountmanagement, W-2, 1099, dividends, interest from loans, taxes exemptdividends, stocks, mutual funds, bonds, capital loss, capital gains,retirement plans, security, rentals, state and local tax refunds,business income, schedule K-1, farm income, business taxes, businessexpenses and credit, sale of business property, gambling winnings,alimony received, jury duty, child's income, sale of home, propertytaxes, mortgage insurance, home energy credits, mortgage interestcredit, home buyer credit, earned income credit, child and dependentcare, adoption credit, child tax credit, donations to charity, carregistration fees, personal property taxes, education expenses, studentloan interest, medical expenses, income taxes, sales tax, foreign taxes,individual retirement account contributions, and job-related expenses,according to one embodiment.

In one embodiment, the topics 260 include the potential search queries216 and include multiple terms. In one embodiment, the topics 260include a single term. In one embodiment, the topics 260 include one ortwo terms but are shorter than a full search query.

The topic terms 262 represent words that are likely to appear in thetopics 260, according to one embodiment. The topic terms 262 have topicterm probabilities 264 of occurring in the topics 260, according oneembodiment. In other words, each of the topic terms 262 has a topic termprobability 264 that defines the probability of a topic term occurringin a topic, according one embodiment. Although each of the topic terms262 may probabilistically occur in more than one of the topics 260, thetopic term probabilities 264 for reoccurring topic terms 262 will likelybe different for different topics 260, according to one embodiment. Aspecific illustrative example of topics 260, topic terms 262, and topicterm probabilities 264 is illustrated in FIG. 3 and is discussed belowin connection with diagram 300, according to one embodiment.

The user profile characteristics probabilities 266 or the user financialdata characteristics probabilities 268 quantify the likelihood that auser will have a question about a topic when one or more of the userprofile characteristics 265 or the user financial data characteristics267 exist, according to one embodiment. The user profile characteristics265 (e.g., whether the user has performed a particular action) areassociated with the user profile characteristics probabilities 266, andthe user financial data characteristics 267 (e.g., whether the userqualifies for or has entered a particular tax-related parameter) areassociated with the user financial data characteristics probabilities268, according to one embodiment. An example of the user profilecharacteristics 265 is a location of a user (e.g., identification of auser experience page that the user is viewing) within a tax returnpreparation interview, according to one embodiment. The user profilecharacteristics probabilities 266 represent a likelihood or aprobabilistic relationship between the topics 260 and the user profilecharacteristics 265, and can be used by the probabilistic topic model238 to add context and certainty to the identification of a particulartopic (e.g., a potential search query) as being relevant to the searchquery terms 226 or to the user, according to one embodiment. The userfinancial data characteristics probabilities 268 represent a likelihoodor a probabilistic relationship between the topics 260 and the userfinancial data characteristics 267, and can be used by the probabilistictopic model 238 to add context and certainty to the identification of aparticular topic as being relevant to the search query terms 226 or tothe user, according to one embodiment. In one embodiment, the predictivemodel 230 or the probabilistic topic model 238 generate the user profilecharacteristics probabilities 266 or the user financial datacharacteristics probabilities 268 by applying one or more predictivealgorithms (e.g., regression, neural networks, Latent Dirichletallocation, or any other presently known or later developed algorithms)to the user financial data 218 and to the user profile data 217.

The search algorithm 228 includes or uses a predictive model 230 fordetermining a likelihood of potential search queries 216, user financialdata 218, or customer self-help content being relevant to the receivedsearch query terms 226 within the context of a particular set of theuser financial data 218 and within the context of particular set of userprofile data 217, according to one embodiment.

The predictive model 230 is trained using one or more prior searchesperformed with the search engine 214, prior search queries submitted byusers to the customer self-help system 110, the user financial data 218,the user profile data 217, and the customer support content 240,according to one embodiment. The predictive model 230 is trained usingpredictive model training operations including, but not limited to, oneor more of regression, logistic regression, decision trees, artificialneural networks, support vector machines, linear regression, nearestneighbor methods, distance based methods, naive Bayes, lineardiscriminant analysis, k-nearest neighbor algorithm, another queryclassifier, and any other presently known or later developed predictivemodel training operations, according to one embodiment.

The customer support content 240 includes a variety of data representingcontent that can be provided in response to a user's search query 129,according to one embodiment. The customer support content 240 includesone or more of crowd-sourced customer support content 242, serviceprovider content 244, definitions content 246, the user financial data218, and the potential search queries 216, according to one embodiment.The crowd-sourced customer support content 242 includes questions andresponses that are submitted by a community of question askers andresponse providers that use the customer self-help system 110 or anotherquestion and answer customer support system that is associated with thefinancial management system 132, according to one embodiment. Thecrowd-sourced customer support content 242 can also be referred to asuser generated content (“UGC”) to distinguish the crowd-sourced customersupport content 242 from the service provider content 244, according toone embodiment. The service provider content 244 includes white papers,questions, answers, frequently asked questions, answers to frequentlyasked questions, tutorials, audio/video content, interactive content, orother content that can be used to assist users in learning about taxes,tax preparation, financial business management, personal financialmanagement, the financial management system 132, or other relevantsubject matter, according to one embodiment. The definitions content 246include acronym definitions, definitions of tax-specific terms (e.g.,tax jargon), definitions of terms that are related to tax law, preparingtax returns, business-specific terms, definitions of terms that arerelated to financial business management, and the financial managementsystem 132, according to one embodiment.

The customer self-help system 110 increases or decreases a priority,rank, or other metricized relevance of the customer support content 240at least partially based on the content relevance data 130, according toone embodiment. In other words, the customer self-help system 110applies recency boosting to the customer support content 240 at leastpartially based on the content relevance data 130, according to oneembodiment. In particular, the customer self-help system 110 increasesor decreases a priority, rank, or other metricized relevance of thecustomer support content 240 at least partially based on one or more ofthe product news data 138, the self-help search trends data 140, the taxlaw updates data 142, and the financial news data 144, according to oneembodiment.

The customer self-help system 110 increases or decreases a priority,rank, or other metricized relevance of the customer support content 240with the content relevance table 118, according to one embodiment. Thecontent relevance table 118 includes content relevance weights 116,which are at least partially based on the content relevance data 130,according to one embodiment. The content relevance weights 116 are ametricized representation of relevance of the topics 260 and of thecustomer support content 240, according to one embodiment. The contentrelevance weights 116 represent whether a group of the topics 260 shouldhave an increased or decreased likelihood of delivery to a user, inresponse to the search query 129, according to one embodiment.Consequently, the content relevance weights 116 represent whether one ormore entries (e.g., user experience pages) in the customer supportcontent 240 should have an increased or decreased likelihood of deliveryto a user, in response to the search query 129, according to oneembodiment.

In one embodiment, all or part of the content relevance table 118 isintegrated into the topics database 258. In one embodiment, the contentrelevance weights 116 are inserted into the topics database 258 as acolumn, to associate the content relevance weights 116 with the topics260.

In one embodiment, all or part of the content relevance table 118 isintegrated into the customer support content 240. In one embodiment, thecontent relevance weights 116 are inserted into the customer supportcontent 240 as a column, to associate the content relevance weights 116with the entries (e.g., user experience pages, potential search queries,service provider content, etc.) in the customer support content 240. Inone embodiment, the customer support content 240 includes contentidentifiers such as identification numbers, content descriptions, andreference numbers that are used to reference user experience pages,documents, questions, answers, definitions, user financial data,potential search queries, and other content for the customer supportcontent 240.

In one embodiment, the content of the topics database 258 and thecontent of the customer support content 240 is maintained in tables,columns, rows, or other entries in a single database (not shown). In oneembodiment, the content relevance weights are included in the singledatabase shared by the content of the topics database 258 and thecontent of the customer support content 240.

In one embodiment, the topics 260 are recency boosted with the contentrelevance weights 116. The content relevance weights 116 are associatedwith the topics 260 to increase or decrease the relevance of each one ofthe topics 260. A specific illustrative example of recency boosting thetopics 260 by applying one or more of the content relevance weights toone or more of the topics 260 is provided in the disclosure of thediagram 300 (shown in FIG. 3).

The customer self-help system 110 uses the content relevance engine 112and the content relevance analytics model 113 to determine or define thecontent relevance weights 116, according to one embodiment. The contentrelevance analytics model 113 includes or uses the probabilistic topicmodel 238, according to one embodiment. The content relevance analyticsmodel 113 updates the content relevance weights 116 dynamically, as thecustomer self-help system 110 acquires or receives the content relevancedata 130, according to one embodiment.

The customer self-help system 110 is wholly or partially stored inmemory 254 (inclusive of non-volatile memory and volatile memory) and iswholly or partially executed by processors 256, according to oneembodiment.

The user computing systems 290 are representative of one or more usercomputing systems that are used by one or more users 294 to provide theuser financial data 218 to the financial management system 132,according to one embodiment. The user computing systems 290 are used toprovide search query terms 226 to the customer self-help system 110(directly or indirectly through the financial management system 132),and are used to receive the recency boosted customer support content 128from the customer self-help system 110 (directly or indirectly throughthe financial management system 132), according to one embodiment.

FIG. 2B illustrates additional features of the financial managementsystem 132 within the production environment 200, according to oneembodiment. The financial management system 132 includes a financialmanagement system engine 272, user experience options 274, and the userfinancial data 218 for progressing users through financial managementsystem content 278 to provide users with one or more financial services282, according to one embodiment.

The financial management system engine 272 provides the user experiencedisplay 276, according to one embodiment. The financial managementsystem engine 272 provides the user experience display 276 to provideusers with financial management system content 278 to deliver one ormore financial services 282 to the users, according to one embodiment.The financial management system engine 272 populates the financialmanagement system content 278 with selected portions of the userexperience options 274, according to one embodiment. The financialmanagement system engine 272 populates the financial management systemcontent 278 with one or more of a variety of the user experience options274, including, but not limited to, one or more tax topics, questions,content sequences, pages, colors, interface elements, promotions, andother multimedia, according to one embodiment.

The financial management system engine 272 uses the user experiencedisplay 276 to both provide the financial management system content 278and to receive user input data 280, according to one embodiment. Theuser input data 280 includes user financial data 218, according to oneembodiment. The user financial data 218 includes financial and personalinformation that financial management system 132 uses to provide one ormore financial service 282 to the users 294, according to oneembodiment.

The financial management system engine 272 or the financial managementsystem 132 uses the user input data 280 (e.g., the user financial data218) to provide financial services 282 such as, preparing a tax return,managing personal finances, and business management, according to oneembodiment.

The financial management system 132 is data that is partially or whollystored in memory 284 (inclusive of non-volatile memory and volatilememory) and is partially or wholly executed by processors 286, accordingto one embodiment.

All or part of the customer self-help system 110 is optionallyintegrated within the financial management system 132 to facilitate theretrieval and sharing of the user financial data 218 or the user profiledata 217, according to one embodiment. In one embodiment, portions ofthe customer self-help system 110 are integrated into the financialmanagement system 132, while the remainder of the customer self-helpsystem 110 operates independently of the financial management system132. In one embodiment, the financial management system 132 hosts theuser experience display 220 (of the customer self-help system 110) whilethe functionality of the customer self-help system 110 that determinesthe recency boosted customer support content 128 operates on computingenvironment resources that are independent of the computing environmentresources of the financial management system 132, according to oneembodiment.

Although the features and functionality of the production environment200 are illustrated or described in terms of individual or modularizedcomponents, engines, modules, models, databases/data stores, or systems,one or more of the functions of one or more of the components, engines,modules, models, databases/data stores, or systems are functionallycombinable with one or more other described or illustrated components,engines, modules, models, databases/data stores, or systems, accordingto one embodiment.

FIG. 3 is a diagram 300 that shows one technique for determining arelevant topic for search query terms received from a user by includinguser profile characteristics and user financial data characteristics,according to one embodiment. The diagram 300 also shows a specificillustrative example of applying content relevance weights to topics toincrease or decrease the relevance of the topics, at least partiallybased on content relevance data, according to one embodiment. In otherwords, the diagram 300 also shows a specific illustrative example ofrecency boosting topics with the content relevance weights that aredetermined or defined using content relevance data, according to oneembodiment. The diagram 300 includes a search query term input element302, e.g., a text box, for receiving search query terms (or a questioncomprising search query terms) from a user, according to one embodiment.The search query term input element 302 is an example of a search queryterm input element, but other embodiments of search query term inputelements (e.g., user interface elements) can be used. The search queryterm input element 302 includes search query terms entered into thesearch query term input element 302, according to one embodiment. Theexample search query terms include a search query term 303, search queryterm 304, search query term 305, and search query term 306, according toone embodiment. Although four example search query terms areillustrated, many more search query terms or fewer search query termscan also be received and analyzed with the customer self-help system toidentify a relevant topic for search query terms, according to oneembodiment.

The diagram 300 (of FIG. 3) includes two specific illustrative exampleentries for the topics database 258 (shown in FIG. 2A) that can be usedto determine a topic that is relevant to the search query terms 303,304, 305, 306, according to one embodiment. The diagram 300 includes atopics database entry 307 and a topics database entry 308, to illustratean example process of determining a relevant, a more relevant, a highestrelevant, or multiple relevant topics for the search query terms 303,304, 305, 306, according to one embodiment. The topics database entry307 includes a topic 309, topic terms 310, and topic term probabilities311, according to one embodiment. The topics database entry 308 includesa topic 312, topic terms combined with context characteristics 313, andtopic term probabilities combined with context characteristicsprobabilities 314, according to one embodiment.

Referring to FIG. 2A and FIG. 3 together, the topics database entries307 and 308 are examples of entries that are made to the topics database258 (shown in FIG. 2A), according to one embodiment. The topics 309 and312 are examples of the topics 260 in the topics database 258, accordingto one embodiment. The topic terms 310 are examples of topic terms 262entered into the topics database 258, according to one embodiment. Thetopic term probabilities 311 are examples of the topic termprobabilities 264 entered into the topics database 258, according to oneembodiment.

Still referring to FIG. 2A and FIG. 3 together, the topic terms combinedwith context characteristics 313 are examples of topic terms 262, userprofile characteristics 265, and user financial data characteristics 267entered into the topics database 258, according to one embodiment. Thetopic term probabilities combined with the context characteristicsprobabilities 314 are examples of the topic term probabilities 264, theuser profile characteristics probabilities 266, and user financial datacharacteristics probabilities 268 entered into the topics database 258,according to one embodiment. The topics 309 and 312 include a numericalidentifier (e.g., topic “18” and topic “19”) and an alphabeticalidentifier (e.g., “INTENT TO CONTACT” topic and “CHANGE/AMEND” topic),according to one embodiment.

Returning to FIG. 3, the topics database entry 308 includes contextcharacteristics of “submitfedreturnpg”, “2014deductionscredits”,“emplymt_status?Y”, “k1form”, “incomeexplore”, and “TRSversion?free”,which represent examples of user profile characteristics and userfinancial data characteristics that may be stored in a topics database,according to one embodiment. User profile characteristics and userfinancial data characteristics are examples of context characteristics.For these example context characteristics, “submitfedreturnpg”represents an example of a user experience display identifier for a userexperience display page for submitting a federal tax return,“2014deductionscredits” represents an example of a user experiencedisplay identifier for a user experience display page for answeringquestions about deductions and credits for 2014 taxes,“emplymt_status?Y” represents a user financial data characteristic forwhether the user is employed, “k1form” represents an example of a userexperience display identifier for a user experience display page forfilling out a K1 tax form, “incomeexplore” represents an example of auser experience display identifier for a user experience display pagefor determining income for a user in a tax return preparation system,and “TRSversion?free” is a user profile characteristic that representswhether a user is using a free version of a tax return preparationsystem, according to one embodiment.

The disclosed customer self-help system determines a topic relevancescore 315 for the topic 309 and a topic relevance score 316 for thetopic 312 by combining or adding the topic term probabilities andcontext characteristics probabilities that are associated with one ormore of the search query terms 303, 304, 305, 306, the user financialdata characteristics, and the user profile characteristics. Thedisclosed customer self-help system receives the search query terms 303,304, 305, 306 from the user in the search query term input element 302,according one embodiment. The disclosed customer self-help systemsearches the topics database entries 307 and 308 for topic terms thatmatch the search query terms 303, 304, 305, 306 and for contextcharacteristics that exist during the submission of the search queryterms 303, 304, 305, 306, according one embodiment. The disclosedcustomer self-help system then associates the topic term probabilities311, 314 of the topic terms 310, 313 that match the search query terms303, 304, 305, 306 and search context, to associate the relevant topicterm probabilities 311, 314, to the search query terms 303, 304, 305,306, according one embodiment. For each topic 309 and 312, the disclosedcustomer self-help system determines the topic relevance scores 315,316, by combining the topic term probabilities 311, 314 that arerelevant to and associated with the search query terms 303, 304, 305,306, user financial data characteristics, and user profilecharacteristics that exist in the topics 309, 312.

As a specific illustrative example, if the search query term 303 is thetopic term “number”, the search query term 304 is the topic term“phone”, the search query term 305 is the topic term “PIN”, the searchquery term 306 is the search query term “service”, the user profilecharacteristic 317 includes “submitfedreturnpg”, and the user financialdata characteristic 320 includes “emplymt_status?Y”, then the topicrelevance score 315 for the topic 309 is 0.192, and the topic relevancescore 316 for the topic 312 is 0.12946, without taking contextcharacteristics into account. If context characteristics are factoredinto the topic relevance score 316 for the topic 312, then the topicrelevance score 316 for the topic 312 increases to 0.30295. Accordingly,the topic relevance score 316 for the topic 312 makes the topic312—“CHANGE/AMEND” more relevant to the search query terms 303, 304,305, 306 than the topic 309—“INTENT TO CONTACT”, because the topicrelevance score 316 is greater than the topic relevance score 315,according to one example of an embodiment.

The diagram 300 includes a content relevance weight 330 and a contentrelevance weight 332, which are applied to the topic relevance scores315 and 316 to recency boost (e.g., increase or decrease) the relevanceof the topics based on content relevance data received by the customerself-help system, according to one embodiment. Although the topicrelevance score 315 is less than (i.e., less relevant than) the topicrelevance score 316, applying the content relevance weights 330 and 332to the topic relevance scores 315 and 316 changes the relevance of thetopics 309 and 312. As a specific illustrative example, if the contentrelevance weight 330 for the topic 309 “INTENT TO CONTACT” has a valueof 0.8, then the weighted relevance score 331 of the topic 309 “INTENTTO CONTACT” has a value of 0.1536. Continuing the specific illustrativeexample, if the content relevance weight 332 for the topic 312“CHANGE/AMEND” has a value of 0.4, then the weighted relevance score 333of the topic 312 “CHANGE/AMEND” has a value of 0.12198. Consequently,weighting the topic relevance scores by the content relevance weights330 and 332 causes the topic 309 “INTENT TO CONTACT” to have a higherweighted relevance score 331 than the weighted relevance score 333 ofthe topic 312 “CHANGE/AMEND” (i.e., 0.1536 is greater than 0.12198).

The disclosed customer self-help system determines more relevant, mostrelevant, or highest relevant topics for the search terms by ranking,sorting, or comparing the topic relevance scores 315, 316 and weightedrelevance scores 331, 333 for each of the topics 309, 312, according toone embodiment. The disclosed customer self-help system determines thatthe topic with the highest weighted relevance score is the highestrelevant topic to the search query terms 303, 304, 305, 306, accordingone embodiment.

Process

FIGS. 4A and 4B are an example flow diagram of a process 400 forimproving search results by recency boosting customer support contentfor a customer self-help system associated with one or more financialmanagement systems, according to one embodiment.

At operation 402, the process begins, according to one embodiment.Operation 402 proceeds to operation 404, according to one embodiment.

At operation 404, the process provides, with one or more computingsystems, a customer self-help system associated with a financialmanagement system, according to one embodiment. Operation 404 proceedsto operation 406, according to one embodiment.

At operation 406, the process stores, with the customer self-helpsystem, customer support content data representing customer supportcontent, in memory allocated for use by the customer self-help system,according to one embodiment. Operation 406 proceeds to operation 408,according to one embodiment.

At operation 408, the process determines content relevance weight databy applying content relevance data to a content relevance analyticsmodel, the content relevance data being acquired by the customerself-help system from one or more sources of content relevance data, thecontent relevance weight data representing content relevance weights,according to one embodiment. Operation 408 proceeds to operation 410,according to one embodiment.

At operation 410, the process applies the content relevance weight datato the customer support content data to transform the customer supportcontent into recency boosted customer support content data, the recencyboosted customer support content data representing recency boostedcustomer support content, according to one embodiment. Operation 410proceeds to operation 412, according to one embodiment.

At operation 412, the process receives search query terms data, by thecustomer self-help system, from a user of the customer self-help system,the search query terms data representing one or more search query termsthat formulate at least part of a search query, according to oneembodiment. Operation 412 proceeds to operation 414, according to oneembodiment.

At operation 414, the process provides search algorithm datarepresenting a search algorithm that matches the search query withportions of the recency boosted customer support content that istopically similar to the search query, according to one embodiment.Operation 414 proceeds to operation 416, according to one embodiment.

At operation 416, the process applies search query data to the searchalgorithm data to identify the portions of the recency boosted customersupport content data that is topically similar to the search query, thesearch query data including the search query terms data, according toone embodiment. Operation 416 proceeds to operation 418, according toone embodiment.

At operation 418, the process updates user experience display data toinclude the portions of the recency boosted customer support contentdata that is topically similar to the search query, the user experiencedisplay data representing a user experience display, according to oneembodiment. Operation 418 proceeds to operation 420, according to oneembodiment.

At operation 420, the process transmits the user experience display datato a user computing system, to display the portions of the recencyboosted customer support content data that is topically similar to thesearch query, in response to the search query terms data received fromthe user, to assist the user in finding an answer to the search query,according to one embodiment. Operation 420 proceeds to operation 422,according to one embodiment.

At operation 422, the process ends, according to one embodiment.

As noted above, the specific illustrative examples discussed above arebut illustrative examples of implementations of embodiments of themethod or process for improving search results by recency boostingcustomer support content for a customer self-help system associated withone or more financial management systems. Those of skill in the art willreadily recognize that other implementations and embodiments arepossible. Therefore, the discussion above should not be construed as alimitation on the claims provided below.

Improving search results by recency boosting customer support contentfor a customer self-help system associated with one or more financialmanagement systems is a technical solution to a long standing technicalproblem of providing irrelevant search results to users, and is not anabstract idea for at least a few reasons. First, improving searchresults by recency boosting customer support content for a customerself-help system associated with one or more financial managementsystems is not an abstract idea because it is not merely an idea itself(e.g., can be performed mentally or using pen and paper). Second,improving search results by recency boosting customer support contentfor a customer self-help system associated with one or more financialmanagement systems is not an abstract idea because it is not afundamental economic practice (e.g., is not merely creating acontractual relationship, hedging, mitigating a settlement risk, etc.).Third, improving search results by recency boosting customer supportcontent for a customer self-help system associated with one or morefinancial management systems is not an abstract idea because it is not amethod of organizing human activity (e.g., managing a game of bingo).Fourth, although mathematics may be used to generate an analytics model,the disclosed and claimed methods and systems of improving searchresults by recency boosting customer support content for a customerself-help system associated with one or more financial managementsystems are not an abstract idea because the methods and systems are notsimply a mathematical relationship/formula but are instead techniquesfor transforming customer support content into data that is more likelyto satisfy user search queries.

Improving search results by recency boosting customer support contentfor a customer self-help system associated with one or more financialmanagement systems is not an abstract idea because the disclosed recencyboosting allows for significant improvement to the technical fields ofuser experience, self-help systems, customer service, customerretention, financial management systems, and domain-specific searchengines, according to one embodiment. The present disclosure addssignificantly to the field of content searching because the disclosedcustomer self-help system: increases the likelihood that a user willreceive an answer to the user's intended question; increases thelikelihood of improving/maintaining a user's trust in the financialmanagement system; and reduces the amount of time users spend searchingfor customer support content that is relevant to the user's searchquery, according to one embodiment.

As a result, embodiments of the present disclosure allow for reduced useof processor cycles, memory, and power consumption, by reducing the timespent by users searching for answers among search results and byreducing time spent by users to repeatedly search for an answer to theirquestion in computing environments, according to one embodiment.Consequently, computing and communication systems implementing orproviding the embodiments of the present disclosure are transformed intomore operationally efficient devices and systems.

In addition to improving overall computing performance, improving searchresults by recency boosting customer support content for a customerself-help system associated with one or more financial managementsystems significantly improves the field of financial managementsystems, by reducing the amount of time it takes to progress a userthrough financial management tasks (e.g., preparing a tax return) due todelays caused by users' unanswered questions, according to oneembodiment. Furthermore, by improving search results by recency boostingcustomer support content for a customer self-help system associated withone or more financial management systems, the disclosed embodiments helpmaintain or build trust and therefore loyalty in the financialmanagement system with which the customer self-help system isassociated, which results in repeat customers, efficient delivery offinancial services, and reduced abandonment of use of the financialmanagement system, according to one embodiment.

In accordance with an embodiment, a computing system implemented methodimproves search results by recency boosting customer support content fora customer self-help system associated with one or more financialmanagement systems. The method includes providing, with one or morecomputing systems, a customer self-help system associated with afinancial management system, according to one embodiment. The methodincludes storing, with the customer self-help system, customer supportcontent data representing customer support content, in memory allocatedfor use by the customer self-help system, according to one embodiment.The method includes determining content relevance weight data byapplying content relevance data to a content relevance analytics model,the content relevance data being acquired by the customer self-helpsystem from one or more sources of content relevance data, the contentrelevance weight data representing content relevance weights, accordingto one embodiment. The method includes applying the content relevanceweight data to the customer support content data to transform thecustomer support content into recency boosted customer support contentdata, the recency boosted customer support content data representingrecency boosted customer support content, according to one embodiment.The method includes receiving search query terms data, by the customerself-help system, from a user of the customer self-help system, thesearch query terms data representing one or more search query terms thatformulate at least part of a search query, according to one embodiment.The method includes providing search algorithm data representing asearch algorithm that matches the search query with portions of therecency boosted customer support content that is topically similar tothe search query, according to one embodiment. The method includesapplying search query data to the search algorithm data to identify theportions of the recency boosted customer support content data that istopically similar to the search query, the search query data includingthe search query terms data, according to one embodiment. The methodincludes updating user experience display data to include the portionsof the recency boosted customer support content data that is topicallysimilar to the search query, the user experience display datarepresenting a user experience display, according to one embodiment. Themethod includes transmitting the user experience display data to a usercomputing system, to display the portions of the recency boostedcustomer support content data that is topically similar to the searchquery, in response to the search query terms data received from theuser, to assist the user in finding an answer to the search query,according to one embodiment.

In accordance with an embodiment, a computing system implemented methodimproves search results by recency boosting customer support content fora customer self-help system associated with one or more financialmanagement systems. The method includes providing, with one or morecomputing systems, a customer self-help system associated with afinancial management system, according to one embodiment. The methodincludes storing, with the customer self-help system, customer supportcontent data representing customer support content, in memory allocatedfor use by the customer self-help system, according to one embodiment.The method includes acquiring, with the customer self-help system,content relevance from one or more sources of content relevance data,according to one embodiment. The method includes determining contentrelevance weight data by applying the content relevance data to acontent relevance analytics model, the content relevance weight datarepresenting content relevance weights, the content relevance analyticsmodel being at least partially based on a probabilistic topic model,according to one embodiment. The method includes applying the contentrelevance weight data to the customer support content data to transformthe customer support content into recency boosted customer supportcontent data, the recency boosted customer support content datarepresenting recency boosted customer support content, according to oneembodiment. The method includes receiving search query terms data, bythe customer self-help system, from a user of the customer self-helpsystem, the search query terms data representing one or more searchquery terms that formulate at least part of a search query, according toone embodiment. The method includes providing search algorithm datarepresenting a search algorithm that matches the search query withportions of the recency boosted customer support content, at leastpartially based on topics that are common to both the search query andthe portions of the recency boosted customer support content, accordingto one embodiment. The method includes applying search query data to thesearch algorithm data to identify the portions of the recency boostedcustomer support content data that topically related to the searchquery, the search query data including the search query terms data,according to one embodiment. The method includes updating userexperience display data to include the portions of the recency boostedcustomer support content data that are topically related to the searchquery, the user experience display data representing a user experiencedisplay, according to one embodiment. The method includes transmittingthe user experience display data to a user computing system, to displaythe portions of the recency boosted customer support content data thatare topically related to the search query, in response to the searchquery terms data received from the user, to assist the user in findingan answer to the search query, according to one embodiment.

In accordance with an embodiment, a system increases a likelihood ofproviding relevant search results by recency boosting customer supportcontent for a customer self-help system associated with one or morefinancial management systems. The system includes one or more processorsand memory, according to one embodiment. The memory includesinstructions which, if executed by the one or more processors, cause theone or more processors to perform a process for improving search resultsby recency boosting customer support content for a customer self-helpsystem associated with one or more financial management systems,according to one embodiment. The process includes providing, a customerself-help system associated with a financial management system,according to one embodiment. The process includes storing, with thecustomer self-help system, customer support content data representingcustomer support content, in memory allocated for use by the customerself-help system, according to one embodiment. The process includesdetermining content relevance weight data by applying content relevancedata to a content relevance analytics model, the content relevance databeing acquired by the self-help system from one or more sources ofcontent relevance data, the content relevance weight data representingcontent relevance weights, according to one embodiment. The processincludes applying the content relevance weight data to the customersupport content data to transform the customer support content intorecency boosted customer support content data, the recency boostedcustomer support content data representing recency boosted customersupport content, according to one embodiment. The process includesreceiving search query terms data, by the customer self-help system,from a user of the customer self-help system, the search query termsdata representing one or more search query terms that formulate at leastpart of a search query, according to one embodiment. The processincludes providing search algorithm data representing a search algorithmthat matches the search query with portions of the recency boostedcustomer support content that is topically similar to the search query,according to one embodiment. The process includes applying search querydata to the search algorithm data to identify the portions of therecency boosted customer support content data that is topically similarto the search query, the search query data including the search queryterms data, according to one embodiment. The process includes updatinguser experience display data to include the portions of the recencyboosted customer support content data that is topically similar to thesearch query, the user experience display data representing a userexperience display, according to one embodiment. The process includestransmitting the user experience display data to a user computingsystem, to display the portions of the recency boosted customer supportcontent data that is topically similar to the search query, in responseto the search query terms data received from the user, to assist theuser in finding an answer to the search query, according to oneembodiment.

In the discussion above, certain aspects of one embodiment includeprocess steps or operations or instructions described herein forillustrative purposes in a particular order or grouping. However, theparticular order or grouping shown and discussed herein are illustrativeonly and not limiting. Those of skill in the art will recognize thatother orders or grouping of the process steps or operations orinstructions are possible and, in some embodiments, one or more of theprocess steps or operations or instructions discussed above can becombined or deleted. In addition, portions of one or more of the processsteps or operations or instructions can be re-grouped as portions of oneor more other of the process steps or operations or instructionsdiscussed herein. Consequently, the particular order or grouping of theprocess steps or operations or instructions discussed herein do notlimit the scope of the invention as claimed below.

As discussed in more detail above, using the above embodiments, withlittle or no modification or input, there is considerable flexibility,adaptability, and opportunity for customization to meet the specificneeds of various users under numerous circumstances.

In the discussion above, certain aspects of one embodiment includeprocess steps or operations or instructions described herein forillustrative purposes in a particular order or grouping. However, theparticular order or grouping shown and discussed herein are illustrativeonly and not limiting. Those of skill in the art will recognize thatother orders or grouping of the process steps or operations orinstructions are possible and, in some embodiments, one or more of theprocess steps or operations or instructions discussed above can becombined or deleted. In addition, portions of one or more of the processsteps or operations or instructions can be re-grouped as portions of oneor more other of the process steps or operations or instructionsdiscussed herein. Consequently, the particular order or grouping of theprocess steps or operations or instructions discussed herein do notlimit the scope of the invention as claimed below.

The present invention has been described in particular detail withrespect to specific possible embodiments. Those of skill in the art willappreciate that the invention may be practiced in other embodiments. Forexample, the nomenclature used for components, capitalization ofcomponent designations and terms, the attributes, data structures, orany other programming or structural aspect is not significant,mandatory, or limiting, and the mechanisms that implement the inventionor its features can have various different names, formats, or protocols.Further, the system or functionality of the invention may be implementedvia various combinations of software and hardware, as described, orentirely in hardware elements. Also, particular divisions offunctionality between the various components described herein are merelyexemplary, and not mandatory or significant. Consequently, functionsperformed by a single component may, in other embodiments, be performedby multiple components, and functions performed by multiple componentsmay, in other embodiments, be performed by a single component.

Some portions of the above description present the features of thepresent invention in terms of algorithms and symbolic representations ofoperations, or algorithm-like representations, of operations oninformation/data. These algorithmic or algorithm-like descriptions andrepresentations are the means used by those of skill in the art to mosteffectively and efficiently convey the substance of their work to othersof skill in the art. These operations, while described functionally orlogically, are understood to be implemented by computer programs orcomputing systems. Furthermore, it has also proven convenient at timesto refer to these arrangements of operations as steps or modules or byfunctional names, without loss of generality.

Unless specifically stated otherwise, as would be apparent from theabove discussion, it is appreciated that throughout the abovedescription, discussions utilizing terms such as, but not limited to,“activating,” “accessing,” “adding,” “aggregating,” “alerting,”“applying,” “analyzing,” “associating,” “calculating,” “capturing,”“categorizing,” “classifying,” “comparing,” “creating,” “defining,”“detecting,” “determining,” “distributing,” “eliminating,” “encrypting,”“extracting,” “filtering,” “forwarding,” “generating,” “identifying,”“implementing,” “informing,” “monitoring,” “obtaining,” “posting,”“processing,” “providing,” “receiving,” “requesting,” “saving,”“sending,” “storing,” “substituting,” “transferring,” “transforming,”“transmitting,” “using,” etc., refer to the action and process of acomputing system or similar electronic device that manipulates andoperates on data represented as physical (electronic) quantities withinthe computing system memories, resisters, caches or other informationstorage, transmission or display devices.

The present invention also relates to an apparatus or system forperforming the operations described herein. This apparatus or system maybe specifically constructed for the required purposes, or the apparatusor system can comprise a general purpose system selectively activated orconfigured/reconfigured by a computer program stored on a computerprogram product as discussed herein that can be accessed by a computingsystem or other device.

The present invention is well suited to a wide variety of computernetwork systems operating over numerous topologies. Within this field,the configuration and management of large networks comprise storagedevices and computers that are communicatively coupled to similar ordissimilar computers and storage devices over a private network, a LAN,a WAN, a private network, or a public network, such as the Internet.

It should also be noted that the language used in the specification hasbeen principally selected for readability, clarity and instructionalpurposes, and may not have been selected to delineate or circumscribethe inventive subject matter. Accordingly, the disclosure of the presentinvention is intended to be illustrative, but not limiting, of the scopeof the invention, which is set forth in the claims below.

In addition, the operations shown in the FIG.s, or as discussed herein,are identified using a particular nomenclature for ease of descriptionand understanding, but other nomenclature is often used in the art toidentify equivalent operations.

Therefore, numerous variations, whether explicitly provided for by thespecification or implied by the specification or not, may be implementedby one of skill in the art in view of this disclosure.

What is claimed is:
 1. A method performed by one or more processors of acustomer self-help system, the method comprising: determining contentrelevance weights for customer support content based at least in part onapplication of content relevance data to a content relevance analyticsmodel; determining topic term probabilities for topic terms assigned toportions of the customer support content; receiving, from a user of thecustomer self-help system, one or more search query terms that formulateat least part of a search query; determining context characteristicsassociated with the user concurrently with receiving the one or moresearch query terms, the context characteristics comprising userfinancial data characteristics and user profile characteristics;determining a probabilistic relationship between topics and the usercontext characteristics; identifying portions of the customer supportcontent that are topically related to the search query based on at leasta portion of the one or more search query terms; determining topicrelevance scores for customer support content associated with the searchquery based on a combination of the topic term probabilities and theprobabilistic relationship between the topics and the user contextcharacteristics; determining weighted relevance scores for customersupport content associated with the search query by multiplying thetopic relevance scores with the content relevance weights associatedwith the customer support content; and transforming the customer supportcontent into recency boosted customer support content based onapplication of the weighted relevance scores to the customer supportcontent.
 2. The method of claim 1, wherein the customer self-help systemis associated with a financial management system comprises one of a taxreturn preparation system, a business financial management system, or apersonal financial management system.
 3. The method of claim 1, whereinthe search algorithm includes an analytics model comprising aprobabilistic topic model.
 4. The method of claim 3, wherein theprobabilistic topic model is configured to determine a topic relevancebetween the customer support content and the one or more search queryterms based at least in part on identifying topics associated with eachcontent entry of the customer support content and identifying topicsassociated with the one or more search query terms.
 5. The method ofclaim 1, further comprising: updating user experience display data toinclude portions of the recency boosted customer support content thatare topically related to the search query; and transmitting the updateduser experience display data to a user computing system.
 6. The methodof claim 1, further comprising: maintaining financial information forusers of the financial management system; and maintaining user profilesfor users of the financial management system, wherein the search querydata includes the user profile data and the user financial data.
 7. Themethod of claim 1, further comprising: determining topic relevancescores for topics associated with the customer support content; sortingthe topic relevance scores; and selecting portions of the customersupport content that are topically relevant to the search query.
 8. Themethod of claim 1, wherein the content relevance data compriseselectronic media feeds retrieved from one or more sources of contentrelevance data, and the method further comprises applying contents ofthe electronic media feeds to a probabilistic topic model to determinetopics of the electronic media feeds.
 9. The method of claim 8, furthercomprising: ranking the topics of the electronic media feeds byprevalence; and defining content relevance weight data for the topics ofthe electronic media feeds based at least in part on the topic rankings.10. The method of claim 1, wherein the search algorithm comprises apredictive model trained using one of a regression, logistic regression,decision trees, artificial neural networks, support vector machines,linear regression, nearest neighbor methods, distance based methods,naive Bayes, linear discriminant analysis, or k-nearest neighboralgorithm.
 11. A customer self-help system, comprising: a database; oneor more processors; and a memory storing instructions that, whenexecuted by the one or more processors, causes the customer self-helpsystem to perform operations comprising: determining content relevanceweights for customer support content based at least in part onapplication of content relevance data to a content relevance analyticsmodel; determining topic term probabilities for topic terms assigned toportions of the customer support content; receiving, from a user of thecustomer self-help system, one or more search query terms that formulateat least part of a search query; determining context characteristicsassociated with the user concurrently with receiving the one or moresearch query terms, the context characteristics comprising userfinancial data characteristics and user profile characteristics;determining a probabilistic relationship between topics and the usercontext characteristics; identifying portions of the customer supportcontent that are topically related to the search query based on at leasta portion of the one or more search query terms; determining topicrelevance scores for customer support content associated with the searchquery based on a combination of the topic term probabilities and theprobabilistic relationship between the topics and the user contextcharacteristics; determining weighted relevance scores for customersupport content associated with the search query by multiplying thetopic relevance scores with the content relevance weights associatedwith the customer support content; and transforming the customer supportcontent into recency boosted customer support content based onapplication of the weighted relevance scores to the customer supportcontent.
 12. The customer self-help system of claim 11, wherein thecustomer self-help system is associated with a financial managementsystem comprises one of a tax return preparation system, a businessfinancial management system, or a personal financial management system.13. The customer self-help system of claim 11, wherein the searchalgorithm includes an analytics model comprising a probabilistic topicmodel.
 14. The customer self-help system of claim 13, wherein theprobabilistic topic model is configured to determine a topic relevancebetween the customer support content and the one or more search queryterms based at least in part on identifying topics associated with eachcontent entry of the customer support content and identifying topicsassociated with the one or more search query terms.
 15. The customerself-help system of claim 11, wherein execution of the instructionscauses the customer self-help system to perform operations furthercomprising: updating user experience display data to include portions ofthe recency boosted customer support content that are topically relatedto the search query; and transmitting the updated user experiencedisplay data to a user computing system.
 16. The customer self-helpsystem of claim 11, wherein execution of the instructions causes thecustomer self-help system to perform operations further comprising:maintaining financial information for users of the financial managementsystem; and maintaining user profiles for users of the financialmanagement system, wherein the search query data includes the userprofile data and the user financial data.
 17. The customer self-helpsystem of claim 11, wherein execution of the instructions causes thecustomer self-help system to perform operations further comprising:determining topic relevance scores for topics associated with thecustomer support content; sorting the topic relevance scores; andselecting portions of the customer support content that are topicallyrelevant to the search query.
 18. The customer self-help system of claim11, wherein the content relevance data comprises electronic media feedsretrieved from one or more sources of content relevance data, and themethod further comprises applying contents of the electronic media feedsto a probabilistic topic model to determine topics of the electronicmedia feeds.
 19. The customer self-help system of claim 18, whereinexecution of the instructions causes the customer self-help system toperform operations further comprising: ranking the topics of theelectronic media feeds by prevalence; and defining content relevanceweight data for the topics of the electronic media feeds based at leastin part on the topic rankings.
 20. The customer self-help system ofclaim 11, wherein the search algorithm comprises a predictive modeltrained using one of a regression, logistic regression, decision trees,artificial neural networks, support vector machines, linear regression,nearest neighbor methods, distance based methods, naive Bayes, lineardiscriminant analysis, or k-nearest neighbor algorithm.